{"id":"W4393492334","doi":"10.5281/zenodo.5172754","title":"Data Storage for Baylis and Boomhower (2022): Fire Characteristics, Expenditures, and Other Miscellaneous Datasets","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Environmental science; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001205685,0.001180184,0.0008931048,0.00389215,0.0007600457,0.003357228,0.002799385,0.001194916,0.2909651],"category_scores_gemma":[0.01160158,0.0007432452,0.0009298521,0.008647889,0.0003341601,0.003512448,0.003162966,0.001884739,0.3026728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002213792,"about_ca_system_score_gemma":0.003053633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02624231,"about_ca_topic_score_gemma":0.02312931,"domain_scores_codex":[0.9989979,0.00007094515,0.0001493519,0.0002628659,0.0003639002,0.0001549957],"domain_scores_gemma":[0.9948263,0.0005702341,0.0006377746,0.001124777,0.002228525,0.000612449],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003104053,0.000005502699,0.0006176176,0.0001126476,0.0000067745,0.000008867692,0.00001995326,0.00008569403,0.00008144442,0.0007495431,0.9949932,0.003287831],"study_design_scores_gemma":[0.00009950012,0.00001068173,0.005350105,0.0002110407,0.00001343183,0.00003027876,0.0001231462,0.000471769,0.001042068,0.002101214,0.9905138,0.00003285515],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001485744,0.00001808287,0.000216007,0.0001744679,0.00005822088,0.00001740547,0.9933447,0.002502286,0.00352034],"genre_scores_gemma":[0.001358246,0.00005147097,0.001146027,0.0001298376,0.0000270065,0.0001228729,0.9913763,0.002073015,0.003715175],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2909651,"threshold_uncertainty_score":0.9733751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0982316219927196,"score_gpt":0.3324923533426531,"score_spread":0.2342607313499335,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}